Predicting the restricted mean event time with the subject's baseline covariates in survival analysis
Predicting the restricted mean event time with the subject's baseline covariates in survival analysis
复制标题
DOI：
10.1093/biostatistics/kxt050
复制
发表时间：
2014-04-01
期刊：
影响因子：
2.1
通讯作者：
Wei, L. J.
中科院分区：
文献类型：
--
作者：
Tian, Lu;Zhao, Lihui;Wei, L. J.
对于设计、监测和分析以事件时间作为结局变量的纵向研究，限制平均事件时间（RMET）是删失情况下生存函数的易于解释且具有临床意义的总结。RMET是直至时间点tau测量的所有潜在事件时间的平均值，并且可以通过Kaplan-Meier曲线下面积在[0，tau]上一致地估计。在本文中，我们研究了一类回归模型，它直接将RMET与其“基线”协变量联系起来，用于预测未来受试者的RMET。由于标准的考克斯和加速失效时间模型也可以用于估计这样的RMET，我们利用交叉验证程序，以选择“最好的”在模型的建立和评估过程中考虑的所有工作模型。最后，我们使用独立的数据集或来自原始数据集的“保留”样本对预测的RMET进行推断，以评估最终选定模型的性能。所有的建议都说明了从艾滋病临床试验组进行的艾滋病毒临床试验和原发性胆汁性肝硬化研究由马约诊所进行的数据。
For designing, monitoring, and analyzing a longitudinal study with an event time as the outcome variable, the restricted mean event time (RMET) is an easily interpretable, clinically meaningful summary of the survival function in the presence of censoring. The RMET is the average of all potential event times measured up to a time point tau and can be estimated consistently by the area under the Kaplan-Meier curve over [0, tau]. In this paper, we study a class of regression models, which directly relates the RMET to its "baseline" covariates for predicting the future subjects' RMETs. Since the standard Cox and the accelerated failure time models can also be used for estimating such RMETs, we utilize a cross-validation procedure to select the "best" among all the working models considered in the model building and evaluation process. Lastly, we draw inferences for the predicted RMETs to assess the performance of the final selected model using an independent data set or a "hold-out" sample from the original data set. All the proposals are illustrated with the data from the an HIV clinical trial conducted by the AIDS Clinical Trials Group and the primary biliary cirrhosis study conducted by the Mayo Clinic.